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Volume 2018, Article ID 3904598, 8 pages
https://doi.org/10.1155/2018/3904598
Research Article

Gear Fault Diagnosis in Variable Speed Condition Based on Multiscale Chirplet Path Pursuit and Linear Canonical Transform

1College of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
2College of Automation, Chongqing University, Chongqing 400044, China

Correspondence should be addressed to Zhang Ke; moc.361@atems

Received 8 November 2017; Revised 15 January 2018; Accepted 7 February 2018; Published 11 March 2018

Academic Editor: Chen Lu

Copyright © 2018 Xu Shuiqing et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. D. McFadden, “Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration,” Journal of Vibration, Acoustics, Stress, and Reliability in Design, vol. 108, no. 2, pp. 165–170, 1986. View at Publisher · View at Google Scholar · View at Scopus
  2. X. Fan and M. J. Zuo, “Gearbox fault detection using Hilbert and wavelet packet transform,” Mechanical Systems and Signal Processing, vol. 20, no. 4, pp. 966–982, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Luo, D. Yu, and M. Liang, “Application of multi-scale chirplet path pursuit and fractional Fourier transform for gear fault detection in speed up and speed-down processes,” Journal of Sound and Vibration, vol. 331, no. 22, pp. 4971–4986, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. P. Girdhar and C. Scheffer, Practical Machinery Vibration Analysis and Predictive Maintenance, vol. 46, Elsevier, 11 edition, 2004.
  5. Y. Yang, Y. He, J. Cheng, and D. Yu, “A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach,” Measurement, vol. 42, no. 4, pp. 542–551, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Feldman, “Hilbert transform in vibration analysis,” Mechanical Systems and Signal Processing, vol. 25, no. 3, pp. 735–802, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. J. He, S. Yang, and C. Gan, “Unsupervised fault diagnosis of a gear transmission chain using a deep belief network,” Sensors, vol. 17, no. 7, article no. 1564, 2017. View at Publisher · View at Google Scholar · View at Scopus
  8. P. Maragos, J. F. Kaiser, and T. F. Quatieri, “Energy separation in signal modulations with application to speech analysis,” IEEE Transactions on Signal Processing, vol. 41, no. 10, pp. 3024–3051, 1993. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Li, J. Zhao, X. Zhang, and H. Teng, “Gear fault diagnosis and damage level identification based on Hilbert transform and Euclidean distance technique,” Journal of Vibro Engineering, vol. 16, no. 8, pp. 4137–4151, 2014. View at Google Scholar · View at Scopus
  10. M. Liang and I. Soltani Bozchalooi, “An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection,” Mechanical Systems and Signal Processing, vol. 24, no. 5, pp. 1473–1494, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Mei, J. Jia, R. Zeng, B. Zhou, and H. Zhao, “A multi-order FRFT self-adaptive filter based on segmental frequency fitting and early fault diagnosis in gears,” Measurement, vol. 91, pp. 532–540, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. P. F. Odgaard and J. Stoustrup, “Gear-box fault detection using time-frequency based methods,” Annual Reviews in Control, vol. 40, pp. 50–58, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. V. Sharma and A. Parey, “Gear crack detection using modified TSA and proposed fault indicators for fluctuating speed conditions,” Measurement, vol. 90, pp. 560–575, 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. X. Tang, Y. Guo, Y. Ding, and H. Zheng, “Application of rolling element bearing envelope analysis based on short time Fourier transition and independent components analysis,” Journal of Mechanical Strength, vol. 34, no. 2, pp. 1–5, 2012. View at Google Scholar
  15. L. Shi, Y. Zhang, and W. Mi, “Application of Wigner-Ville-distribution-based spectral kurtosis algorithm to fault diagnosis of rolling bearing,” Journal of Vibration Measurement & Diagnosis, vol. 31, no. 1, pp. 27–31, 2011. View at Google Scholar · View at Scopus
  16. D. Song, C. Lu, and J. Ma, “Gearbox fault diagnosis based on VMD-MSE and adaboost classifier,” Vibroengineering Procedia, vol. 14, pp. 120–125, 2017. View at Publisher · View at Google Scholar
  17. C. W. A. W. Fanlei, “Gear fault diagnosis based on LCD and LME demodulation approach,” China Mechanical Engineering, vol. 27, no. 24, p. 3332.
  18. H. Yuan and C. Lu, “Rolling bearing fault diagnosis under fluctuant conditions based on compressed sensing,” Structural Control and Health Monitoring, vol. 24, no. 5, Article ID e1918, 2017. View at Publisher · View at Google Scholar · View at Scopus
  19. G. Cheng, X. Chen, H. Li, P. Li, and H. Liu, “Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposition,” Measurement, vol. 91, pp. 140–154, 2016. View at Publisher · View at Google Scholar · View at Scopus
  20. F.-C. Zhou, G.-J. Tang, and Y.-L. He, “An Effective Gear Fault Diagnosis Method Based on Singular Value Decomposition and Frequency Slice Wavelet Transform,” International Journal of Rotating Machinery, vol. 2016, Article ID 7458956, 8 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Krishnakumari, A. Elayaperumal, M. Saravanan, and C. Arvindan, “Fault diagnostics of spur gear using decision tree and fuzzy classifier,” The International Journal of Advanced Manufacturing Technology, vol. 89, no. 9-12, pp. 3487–3494, 2017. View at Publisher · View at Google Scholar · View at Scopus
  22. Z. Xing, J. Qu, Y. Chai, Q. Tang, and Y. Zhou, “Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine,” Journal of Mechanical Science and Technology, vol. 31, no. 2, pp. 545–553, 2017. View at Publisher · View at Google Scholar · View at Scopus
  23. P. V. Kane and A. B. Andhare, “Application of psychoacoustics for gear fault diagnosis using artificial neural network,” Journal of Low Frequency Noise, Vibration and Active Control, vol. 35, no. 3, pp. 207–220, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Wu and C. Z. Song, “Engine ggearbox fault diagnosis using modified Elman neural network and ACO algorithm,” Applied Mechanics and Materials, vol. 190-191, pp. 982–986, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. T. Waqar and M. Demetgul, “Thermal analysis MLP neural network based fault diagnosis on worm gears,” Measurement, vol. 86, pp. 56–66, 2016. View at Publisher · View at Google Scholar · View at Scopus
  26. X. You and W. Zhang, “Fault diagnosis of frequency converter in wind power system based on SOM neural network,” in Proceedings of the 2012 International Workshop on Information and Electronics Engineering, IWIEE 2012, pp. 3132–3136, China, March 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. W. Zhao, D. Siegel, J. Lee, and L. Su, “An integrated framework of drivetrain degradation assessment and fault localization for offshore wind turbines,” International Journal of Prognostics and Health Management, vol. 4, 2, pp. 462–472, 2013. View at Google Scholar · View at Scopus
  28. C. Lu, L. Tao, and H. Fan, “An intelligent approach to machine component health prognostics by utilizing only truncated histories,” Mechanical Systems and Signal Processing, vol. 42, no. 1-2, pp. 300–313, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Moshinsky and C. Quesne, “Linear canonical transformations and their unitary representations,” Journal of Mathematical Physics, vol. 12, pp. 1772–1780, 1971. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  30. T. Alieva and M. J. Bastiaans, “Properties of the linear canonical integral transformation,” Journal of the Optical Society of America A: Optics and Image Science, and Vision, vol. 24, no. 11, pp. 3658–3665, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. Y.-N. Zhang and B.-Z. Li, “ϕ-linear canonical analytic signals,” Signal Processing, vol. 143, pp. 181–190, 2018. View at Publisher · View at Google Scholar · View at Scopus
  32. S. Xu, Y. Chai, Y. Hu, C. Jiang, and Y. Li, “Reconstruction of digital spectrum from periodic nonuniformly sampled signals in offset linear canonical transform domain,” Optics Communications, vol. 348, pp. 59–65, 2015. View at Publisher · View at Google Scholar · View at Scopus
  33. R. Tao, B.-Z. Li, Y. Wang, and G. Aggrey, “On sampling of band-limited signals associated with the linear canonical transform,” IEEE Transactions on Signal Processing, vol. 56, no. 11, pp. 5454–5464, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  34. S. Xu, Y. Chai, and Y. Hu, “Spectral analysis of sampled band-limited signals in the offset linear canonical transform domain,” Circuits, Systems and Signal Processing, vol. 34, no. 12, pp. 3979–3997, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  35. X.-N. Xu, B.-Z. Li, and X.-L. Ma, “Instantaneous frequency estimation based on the linear canonical transform,” Journal of The Franklin Institute, vol. 349, no. 10, pp. 3185–3193, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  36. Q. Feng and B.-Z. Li, “Convolution and correlation theorems for the two-dimensional linear canonical transform and its applications,” IET Signal Processing, vol. 10, no. 2, pp. 125–132, 2016. View at Publisher · View at Google Scholar · View at Scopus
  37. Y. Guo and B.-Z. Li, “Blind image watermarking method based on linear canonical wavelet transform and QR decomposition,” IET Image Processing, vol. 10, no. 10, pp. 773–786, 2016. View at Publisher · View at Google Scholar · View at Scopus
  38. E. J. Candès, P. R. Charlton, and H. Helgason, “Detecting highly oscillatory signals by chirplet path pursuit,” Applied and Computational Harmonic Analysis , vol. 24, no. 1, pp. 14–40, 2008. View at Publisher · View at Google Scholar · View at Scopus
  39. J. Luo, S. Zhang, M. Zhong, and Z. Lin, “Order spectrum analysis for bearing fault detection via joint application of synchrosqueezing transform and multiscale chirplet path pursuit,” Shock and Vibration, vol. 2016, Article ID 2976389, 11 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus